AI Article Synopsis

  • Scientific literature has rapidly expanded over the last century, yet measuring and understanding knowledge within this vast output remains challenging due to its abstract nature.
  • A new Knowledge Quantification Index (KQI) is introduced, which uses the structure of citation networks and the concept of disorder to quantify knowledge production in literature.
  • KQI consolidates various scientific impact metrics, improves interpretability, and mitigates manipulation, offering a fresh perspective on knowledge measurement through entropy and structural analysis.

Article Abstract

Scientific literature, as the major medium that carries knowledge between scientists, exhibits explosive growth in the last century. Despite the frequent use of many tangible measures, to quantify the influence of literature from different perspectives, it remains unclear how knowledge is embodied and measured among tremendous scientific productivity, as knowledge underlying scientific literature is abstract and difficult to concretize. In this regard, there has laid a vacancy in the theoretical embodiment of knowledge for their evaluation and excavation. Here, for the first time, we quantify the knowledge from the perspective of information structurization and define a new measure of knowledge quantification index (KQI) that leverages the extent of disorder difference caused by hierarchical structure in the citation network to represent knowledge production in the literature. Built upon 214 million articles, published from 1800 to 2021, KQI is demonstrated for mining influential classics and laureates that are omitted by traditional metrics, thanks to in-depth utilization of structure. Due to the additivity of entropy and the interconnectivity of the network, KQI assembles numerous scientific impact metrics into one and gains interpretability and resistance to manipulation. In addition, KQI explores a new perspective regarding knowledge measurement through entropy and structure, utilizing structure rather than semantics to avoid ambiguity and attain applicability.

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Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279314PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9812334PMC

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